Soil infiltration based on bp neural network and grey relational analysis

Soil infiltration is a key link of the natural water cycle process. Studies on soil permeability are conducive for water resources assessment and estimation, runoff regulation and management, soil erosion modeling, nonpoint and point source pollution of farmland, among other aspects. The unequal influence of rainfall duration, rainfall intensity, antecedent soil moisture, vegetation cover, vegetation type, and slope gradient on soil cumulative infiltration was studied under simulated rainfall and different underlying surfaces. We established a six factor-model of soil cumulative infiltration by the improved back propagation (BP)-based artificial neural network algorithm with a momentum term and self-adjusting learning rate. Compared to the multiple nonlinear regression method, the stability and accuracy of the improved BP algorithm was better. Based on the improved BP model, the sensitive index of these six factors on soil cumulative infiltration was investigated. Secondly, the grey relational analysis method was used to individually study grey correlations among these six factors and soil cumulative infiltration. The results of the two methods were very similar. Rainfall duration was the most influential factor, followed by vegetation cover, vegetation type, rainfall intensity and antecedent soil moisture. The effect of slope gradient on soil cumulative infiltration was not significant.

Reversal of activity-mediated spine dynamics and learning impairment in a mouse model of Fragile X syndrome.

Fragile X syndrome (FXS) is characterized by intellectual disability and autistic traits, and results from the silencing of the FMR1 gene coding for a protein implicated in the regulation of protein synthesis at synapses. The lack of functional Fragile X mental retardation protein has been proposed to result in an excessive signaling of synaptic metabotropic glutamate receptors, leading to alterations of synapse maturation and plasticity. It remains, however, unclear how mechanisms of activity-dependent spine dynamics are affected in Fmr knockout (Fmr1-KO) mice and whether they can be reversed. Here we used a repetitive imaging approach in hippocampal slice cultures to investigate properties of structural plasticity and their modulation by signaling pathways. We found that basal spine turnover was significantly reduced in Fmr1-KO mice, but markedly enhanced by activity. Additionally, activity-mediated spine stabilization was lost in Fmr1-KO mice. Application of the metabotropic glutamate receptor antagonist α-Methyl-4-carboxyphenylglycine (MCPG) enhanced basal turnover, improved spine stability, but failed to reinstate activity-mediated spine stabilization. In contrast, enhancing phosphoinositide-3 kinase (PI3K) signaling, a pathway implicated in various aspects of synaptic plasticity, reversed both basal turnover and activity-mediated spine stabilization. It also restored defective long-term potentiation mechanisms in slices and improved reversal learning in Fmr1-KO mice. These results suggest that modulation of PI3K signaling could contribute to improve the cognitive deficits associated with FXS.

Regulation of the integration of newly generated neurons in the adult hippocampus

Hippocampal adult neurogenesis results in the continuous formation of new neurons in the adult hippocampus, which participate to learning and memory. Manipulations increasing adult neurogenesis have a huge clinical potential in pathologies involving memory loss. Intringuingly, most of the newborn neurons die during their maturation. Thus, increasing newborn neuron survival during their maturation may be a powerful way to increase overall adult neurogenesis. The factors governing this neuronal death are yet poorly known. In my PhD project, we made the hypothesis that synaptogenesis and synaptic activity play a role in the survival of newborn hippocampal neurons. We studied three factors potentially involved in the regulation of the synaptic integration of adult-born neurons. First, we used propofol anesthesia to provoke a global increase in GABAergic activity of the network, and we evaluated the outcome on newborn neuron synaptic integration, morphological development and survival. Propofol anesthesia impaired the dendritic maturation and survival of adult-born neurons in an age-dependent manner. Next, we examined the development of astrocytic ensheathment on the synapses formed by newborn neurons, as we hypothesized that astrocytes are involved in their synaptic integration. Astrocytic processes ensheathed the synapses of newborn neurons very early in their development, and the processes modulated synaptic transmission on these cells. Finally, we studied the cell-autonomous effects of the overexpression of synaptic adhesion molecules on the development, synaptic integration and survival of newborn neurons, and we found that manipulating of a single adhesion molecule was sufficient to modify synaptogenesis and/or synapse function, and to modify newborn neuron survival. Together, these results suggest that the activity of the neuronal network, the modulation of glutamate transport by astrocytes, and the synapse formation and activity of the neuron itself may regulate the survival of newborn neurons. Thus, the survival of newborn neurons may depend on their ability to communicate with the network. This knowledge is crucial for finding ways to increase neurogenesis in patients. More generally, understanding how the neurogenic niche works and which factors are important for the generation, maturation and survival of neurons is fundamental to be able to maybe, one day, replace neurons in any region of the brain.

Penser la médecine communautaire s'apprend avec les pairs. [The spirit of community medicine: students learning from students].

At the University of Lausanne third-year medical students are given the task of spending a month investigating a question of community medicine. In 2009, four students evaluated the legitimacy of health insurers intervening in the management of depression. They found that health insurers put pressure on public authorities during the development of legislation governing the health system and reimbursement for treatment. This fact emerged during the scientific investigation led jointly by the team in the course of the "module of immersion in community medicine." This paper presents each step of their study. The example chosen illustrates the learning objectives covered by the module.

The Arts (Music, Arts&Crafts and Drama) as a procedure for learning pre-school content: Grupo Edebé Project

There is nothing as amazing and fascinating as children learning process. Between 0 and 6 years old, a child brain develops in a waythat will never be repeated. At this age, children are eager to discover and they have great potential of active and affective life.Because of this, their learning capacity in this period is incalculable. (Jordan-Decarbo y Nelson, 2002; Wild, 1999).Pre-school Education is a unique and special stage, with self identity, which aims are:attending children as a whole,motivate them to learn,give them an affective and stable environment in which they can grow up and get to be balanced and confident people and inwhich they can relate to others, learn, enjoy and be happy.Arts, Music, Visual Arts and Drama (Gardner, 1994) can provide a framework of special, even unique, personal expression.With the aim of introducing qualitative improvements in the education of children and to ensure their emotional wellbeing, and havingnoticed that teachers had important needs and concerns as regards to diversity in their student groups, we developed a programbased on the detection of needs and concerns explained by professionals in education.This program of Grupo edebé, object of our research, is a multicultural, interdisciplinary and globalizing project the aims of which are:developing children's talent and personality,keeping their imagination and creativity and using these as a learning resource,promoting reasoning, favouring expression and communication,providing children with the tools to manage their emotions,and especially, introducing Arts as a procedure to increase learning.We wanted to start the research by studying the impact (Brice, 2003) that this last point had on the learning of five-year-old childrenschooled in multicultural environments.Therefore, the main goal of the research was the assessment of the implementation of a child education programme attending todiversity in a population of five-year-old children, specifically in the practice of procedures based on the use of Arts (music, arts andcrafts and theatre) as a vehicle or procedure for learning contents in Pre-school stage.Because children emotional welfare was a subject of our concern, and bearing in mind that the affective aspects are of vitalimportance for learning and child development (Parke and Gauvain, 2009), Grupo Edebé has also evaluated the starting, evolving andfinal impact in five-year-old children given that they finish Pre-school education at that age.

The Learning Affect Monitor (LAM) - A computer-based system integrating quantitative and qualitative assessment of affective states in daily life.

The Learning Affect Monitor (LAM) is a new computer-based assessment system integrating basic dimensional evaluation and discrete description of affective states in daily life, based on an autonomous adapting system. Subjects evaluate their affective states according to a tridimensional space (valence and activation circumplex as well as global intensity) and then qualify it using up to 30 adjective descriptors chosen from a list. The system gradually adapts to the user, enabling the affect descriptors it presents to be increasingly relevant. An initial study with 51 subjects, using a 1 week time-sampling with 8 to 10 randomized signals per day, produced n = 2,813 records with good reliability measures (e.g., response rate of 88.8%, mean split-half reliability of .86), user acceptance, and usability. Multilevel analyses show circadian and hebdomadal patterns, and significant individual and situational variance components of the basic dimension evaluations. Validity analyses indicate sound assignment of qualitative affect descriptors in the bidimensional semantic space according to the circumplex model of basic affect dimensions. The LAM assessment module can be implemented on different platforms (palm, desk, mobile phone) and provides very rapid and meaningful data collection, preserving complex and interindividually comparable information in the domain of emotion and well-being.

Recent advances in the epidermal growth factor receptor/ligand system biology on skin homeostasis and keratinocyte stem cell regulation.

The epidermal growth factor (EGF) receptor/ligand system stimulates multiple pathways of signal transduction, and is activated by various extracellular stimuli and inter-receptor crosstalk signaling. Aberrant activation of EGF receptor (EGFR) signaling is found in many tumor cells, and humanized neutralizing antibodies and synthetic small compounds against EGFR are in clinical use today. However, these drugs are known to cause a variety of skin toxicities such as inflammatory rash, skin dryness, and hair abnormalities. These side effects demonstrate the multiple EGFR-dependent homeostatic functions in human skin. The epidermis and hair follicles are self-renewing tissues, and keratinocyte stem cells are crucial for maintaining these homeostasis. A variety of molecules associated with the EGF receptor/ligand system are involved in epidermal homeostasis and hair follicle development, and the modulation of EGFR signaling impacts the behavior of keratinocyte stem cells. Understanding the roles of the EGF receptor/ligand system in skin homeostasis is an emerging issue in dermatology to improve the current therapy for skin disorders, and the EGFR inhibitor-associated skin toxicities. Besides, controlling of keratinocyte stem cells by modulating the EGF receptor/ligand system assures advances in regenerative medicine of the skin. We present an overview of the recent progress in the field of the EGF receptor/ligand system on skin homeostasis and regulation of keratinocyte stem cells.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Open Educational Resources: an opportunity for virtual learning institutions

The Universitat Oberta de Catalunya (UOC, Open University of Catalonia) is involved inseveral research projects and educational activities related to the use of Open Educational Resources (OER). Some of the discussed issues in the concept of OER are research issues which are being tackled in two EC projects (OLCOS and SELF). Besides the research part, the UOC aims at developing a virtual centre for analysing and promoting the concept of OERin Europe in the sector of Higher and Further Education. The objectives are to makeinformation and learning services available to provide university management staff,eLearning support centres, faculty and learners with practical information required to create, share and re-use such interoperable digital content, tools and licensing schemes. In the realisation of these objectives, the main activities are the following: to provide organisationaland individual e-learning end-users with orientation; to develop perspectives and useful recommendations in the form of a medium-term Roadmap 2010 for OER in Higher and Further Education in Europe; to offer practical information and support services about how to create, share and re-use open educational content by means of tutorials, guidelines, best practices, and specimen of exemplary open e-learning content; to establish a larger group ofcommitted experts throughout Europe and other continents who not only share theirexpertise but also steer networking, workshops, and clustering efforts; and to foster and support a community of practice in open e-learning content know-how and experiences.

Emotions in a Web-based Learning Environment

The aim of this thesis was to examine emotions in a web-based learning environment (WBLE). Theoretically, the thesis was grounded on the dimensional model of emotions. Four empirical studies were conducted. Study I focused on students’ anxiety and their self-efficacy in computer-using situations. Studies II and III examined the influence of experienced emotions on students’ collaborative visible and non-collaborative invisible activities and lurking in a WBLE. Study II also focused on the antecedents of the emotions students experience in a web-based learning environment. Study IV concentrated on clarifying the differences between emotions experienced in face-to-face and web-based collaborative learning. The results of these studies are reported in four original research articles published in scientific journals. The present studies demonstrate that emotions are important determinants of student behaviour in a web-based learning, and justify the conclusion that interactions on the web can and do have an emotional content. Based on the results of these empirical studies, it can be concluded that the emotions students experience during the web-based learning result mostly from the social interactions rather than from the technological context. The studies indicate that the technology itself is not the only antecedent of students’ emotional reactions in the collaborative web-based learning situations. However, the technology itself also exerted an influence on students’ behaviour. It was found that students’ computer anxiety was associated with their negative expectations of the consequences of using technology-based learning environments in their studies. Moreover, the results also indicated that student behaviours in a WBLE can be divided into three partially overlapping classes: i) collaborative visible ii) non-collaborative invisible activities, and iii) lurking. What is more, students’ emotions experienced during the web-based learning affected how actively they participated in such activities in the environment. Especially lurkers, i.e. students who seldom participated in discussions but frequently visited the online environment, experienced more negatively valenced emotions during the courses than did the other students. This result indicates that such negatively toned emotional experiences can make the lurking individuals less eager to participate in other WBLE courses in the future. Therefore, future research should also focus more precisely on the reasons that cause individuals to lurk in online learning groups, and the development of learning tasks that do not encourage or permit lurking or inactivity. Finally, the results from the study comparing emotional reactions in web-based and face-to-face collaborative learning indicated that the learning by means of web-based communication resulted in more affective reactivity when compared to learning in a face-to-face situation. The results imply that the students in the web-based learning group experienced more intense emotions than the students in the face-to-face learning group.The interpretations of this result are that the lack of means for expressing emotional reactions and perceiving others’ emotions increased the affectivity in the web-based learning groups. Such increased affective reactivity could, for example, debilitate individual’s learning performance, especially in complex learning tasks. Therefore, it is recommended that in the future more studies should be focused on the possibilities to express emotions in a text-based web environment to ensure better means for communicating emotions, and subsequently, possibly decrease the high level of affectivity. However, we do not yet know whether the use of means for communicating emotional expressions via the web (for example, “smileys” or “emoticons”) would be beneficial or disadvantageous in formal learning situations. Therefore, future studies should also focus on assessing how the use of such symbols as a means for expressing emotions in a text-based web environment would affect students’ and teachers’ behaviour and emotional state in web-based learning environments.

c-Myc controls the balance between hematopoietic stem cell self-renewal and differentiation.

The activity of adult stem cells is essential to replenish mature cells constantly lost due to normal tissue turnover. By a poorly understood mechanism, stem cells are maintained through self-renewal while concomitantly producing differentiated progeny. Here, we provide genetic evidence for an unexpected function of the c-Myc protein in the homeostasis of hematopoietic stem cells (HSCs). Conditional elimination of c-Myc activity in the bone marrow (BM) results in severe cytopenia and accumulation of HSCs in situ. Mutant HSCs self-renew and accumulate due to their failure to initiate normal stem cell differentiation. Impaired differentiation of c-Myc-deficient HSCs is linked to their localization in the differentiation preventative BM niche environment, and correlates with up-regulation of N-cadherin and a number of adhesion receptors, suggesting that release of HSCs from the stem cell niche requires c-Myc activity. Accordingly, enforced c-Myc expression in HSCs represses N-cadherin and integrins leading to loss of self-renewal activity at the expense of differentiation. Endogenous c-Myc is differentially expressed and induced upon differentiation of long-term HSCs. Collectively, our data indicate that c-Myc controls the balance between stem cell self-renewal and differentiation, presumably by regulating the interaction between HSCs and their niche.

Functional independence within the self-memory system: new insights from two cases of developmental amnesia.

Neuropsychological and neuroimaging data suggest that the self-memory system can be fractionated into three functionally independent systems processing personal information at several levels of abstraction, including episodic memories of one's life (episodic autobiographical memory, EAM), semantic knowledge of facts about one's life (semantic autobiographical memory, SAM), and semantic knowledge of one's personality [conceptual self, (CS)]. Through the study of two developmental amnesic patients suffering of neonatal brain injuries, we explored how the different facets of the self-memory system develop when growing up with bilateral hippocampal atrophy. Neuropsychological evaluations showed that both of them suffered from dramatic episodic learning disability with no sense of recollection (Remember/Know procedure), whereas their semantic abilities differed, being completely preserved (Valentine) or not (Jocelyn). Magnetic resonance imaging, including quantitative volumetric measurements of the hippocampus and adjacent (entorhinal, perirhinal, and temporopolar) cortex, showed severe bilateral atrophy of the hippocampus in both patients, with additional atrophy of adjacent cortex in Jocelyn. Exploration of EAM and SAM according to lifetime periods covering the entire lifespan (TEMPAu task, Piolino et al., 2009) showed that both patients had marked impairments in EAM, as they lacked specificity, details and sense of recollection, whereas SAM was completely normal in Valentine, but impaired in Jocelyn. Finally, measures of patients' CS (Tennessee Self-Concept Scale, Fitts and Warren, 1996), checked by their mothers, were generally within normal range, but both patients showed a more positive self-concept than healthy controls. These two new cases support a modular account of the medial-temporal lobe with episodic memory and recollection depending on the hippocampus, and semantic memory and familiarity on adjacent cortices. Furthermore, they highlight developmental episodic and semantic functional independence within the self-memory system suggesting that SAM and CS may be acquired without episodic memories.

Evaluación de la educación emocional en el ciclo medio de Educación Primaria

El principal propósito de la educación consiste en favorecer el desarrollo integral de la persona, lo cual implica atender el aspecto cognitivo y afectivo. Tradicionalmente, se han priorizado sólo los contenidos cognitivos por este motivo queremos incidir sobre los afectos. El objetivo general de este trabajo de investigación que presentamos consiste en aplicar y evaluar un Programa de Educación Emocional (PEEP), integrado dentro del currículum de Primaria de ciclo medio que ayude a prevenir los efectos nocivos de las emociones negativas y facilite la relación consigo mismo y con los demás. Objetivo general que se constatara a partir de cuatro criterios: conseguir un mejor conocimiento de las propias emociones y de las emociones de los demás, desarrollar estrategias de regulación emocional, mejorar la autoestima, aprender habilidades de vida y socioemocionales. Pues, la finalidad es intervenir en la mejora de la educación emocional de los alumnos entre los 8 y los 10 años, en total 510 alumnos de los cuales 104 formaron la muestra de investigación. Fueron divididos en dos grupos el grupo experimental y el grupo control, ambos constituían una muestra homogénea y estadísticamente comparable por lo que podíamos plantearnos una intervención y valorar su incidencia. Los resultados indican que mejora el nivel de Educación Emocional (EE) de estos alumnos con un nivel de significación de p= 0,001. A partir de este momento, podemos concretar que los resultados conseguidos por los alumnos que han seguido la aplicación del programa mejoran significativamente. Este resultado, nos permite afirmar que el uso intencional del programa de educación emocional para mejorar el ámbito afectivo ha incidido positivamente en el proceso de aprendizaje de los alumnos. Sin embargo consideramos esencial llevar a cabo una formación y posterior evaluación del profesorado en EE, como paso previo necesario para una aplicación óptima del programa.

Transferring control demands across incidental learning tasks - Stronger sequence usage in serial reaction task after shortcut option in letter string checking

After incidentally learning about a hidden regularity, participants can either continue to solve the task as instructed or, alternatively, apply a shortcut. Past research suggests that the amount of conflict implied by adopting a shortcut seems to bias the decision for vs. against continuing instruction-coherent task processing. We explored whether this decision might transfer from one incidental learning task to the next. Theories that conceptualize strategy change in incidental learning as a learning-plus-decision phenomenon suggest that high demands to adhere to instruction-coherent task processing in Task 1 will impede shortcut usage in Task 2, whereas low control demands will foster it. We sequentially applied two established incidental learning tasks differing in stimuli, responses and hidden regularity (the alphabet verification task followed by the serial reaction task, SRT). While some participants experienced a complete redundancy in the task material of the alphabet verification task (low demands to adhere to instructions), for others the redundancy was only partial. Thus, shortcut application would have led to errors (high demands to follow instructions). The low control demand condition showed the strongest usage of the fixed and repeating sequence of responses in the SRT. The transfer results are in line with the learning-plus-decision view of strategy change in incidental learning, rather than with resource theories of self-control.

Factors Related to Study Success at Lappeenranta University of Technology

Tutkimuksen avulla selvitettiin opintomenestykseen liittyviä tekijöitä Lappeenrannan teknillisessä korkeakoulussa (LTKK). Tutkimus liittyi opetuksen ja oppimisen kehitystyöhön tuotantotalouden osastolla. Tutkimuksen viitekehyksen muodosti oppimistuloksia selittävä malli, joka on laadittu Tynjälän (1999) kokoaman mallin perusteella. Tutkimuksen perusjoukko muodostui LTKK:n läsnä olevista perusopiskelijoista lukuun ottamatta jatko- ja vaihto-opiskelijoita. Opiskelijat jaettiin ositetulla otannalla ryhmiin, joissa suoritettiin yksinkertainen satunnaisotanta. Otoskoko oli 645 opiskelijaa. Tiedonkeruumenetelmänä oli Internet-kysely. Aineisto analysoitiin useiden kvantitatiivisten ja kvalitatiivisten menetelmien avulla. Tutkimuksen tuloksia voidaan pitää luotettavina ja tutkimuksen avulla saatiin tärkeää ja hyödyllistä tietoa opintomenestyksestä ja oppimisprosesseista. Tulosten perusteella merkittävimmät oppimistuloksiin positiivisesti liittyvät tekijät ovat syväsuuntautunut opiskelustrategia ja luottaminen omiin kykyihin, ja negatiiviset tekijät ovat oppimisen itsesäätelyn puute, omien kykyjen epäily ja pintasuuntautunut opiskelustrategia. Merkitysorientoituneet, itsesäätelykykyiset opiskelijat menestyivät LTKK:ssa parhaiten.